Enhancing aspect-based multi-labeling with ensemble learning for ethical logistics

In the dynamic domain of logistics, effective communication is essential for streamlined operations. Our innovative solution, the Multi-Labeling Ensemble (MLEn), tackles the intricate task of extracting multi-labeled data, employing advanced techniques for accurate preprocessing of textual data thro...

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Veröffentlicht in:	PloS one 2024-05, Vol.19 (5), p.e0295248-e0295248
Hauptverfasser:	Almazroi, Abdulwahab Ali, Ayub, Nasir
Format:	Artikel
Sprache:	eng
Schlagworte:	Accuracy Algorithms Analysis Biology and Life Sciences Classification Communication Computational linguistics Computer and Information Sciences Data mining Datasets Efficiency Electronic commerce Emotions Ensemble learning Ethical standards Ethics Evaluation Feature extraction Humans Information management Labeling Labels Language processing Learning strategies Literature reviews Logistics Machine Learning Medical ethics Medicine and Health Sciences Natural language interfaces Natural Language Processing Physical Sciences Preprocessing Rankings Research and Analysis Methods Semantics Sentiment analysis Social Sciences User generated content
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